{
 "cells": [
  {
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   "source": [
    "# 17 - 预测模型 101\n",
    "\n",
    "我们将离开本书的第一部分。那部分涵盖了因果推理的核心。那里的技术是众所周知和成熟的。他们经受住了时间的考验。第一部分建立了我们可以依赖的坚实基础。用更专业的术语来说，第一部分侧重于定义什么是因果推理，哪些偏差会阻止相关性成为因果关系，调整这些偏差的多种方法（回归、匹配和倾向得分）和规范识别策略（工具变量、双重差分 和 断点回归）。总之，第一部分重点介绍了我们用来确定平均干预效果 \\\\(E[Y_1 - Y_0]\\\\) 的标准技术。\n",
    " \n",
    "当我们进入第二部分时，事情会变得有点不稳定。我们将介绍因果推理文献的最新发展、它与机器学习的关系以及行业中的应用。从这个意义上说，我们在学术严谨性与适用性和经验主义之间进行了权衡。第 II 部分中介绍的一些方法没有关于它们为什么起作用的可靠理论。尽管如此，当我们尝试它们时，它们似乎仍然有效。从这个意义上说，对于想要在日常工作中使用因果推理的行业从业者，而不是想要研究世界上基本因果关系的科学家，第二部分可能更有用。\n",
    " \n",
    "第二部分的前几章将侧重于估计异质治疗效果。我们将从一个只关心平均治疗效果 \\\\(E[Y_1 - Y_0]\\\\) 的世界转移到一个我们想知道不同单位对治疗有何不同反应的世界\\\\(E[ Y_1 - Y_0 | X]\\\\)。这是个性化至关重要的世界。我们希望优先治疗那些治疗效果最有影响的人，我们不想治疗那些治疗对他们有害的人。从某种意义上说，我们也在从一个关于平均治疗效果的积极问题转向一个规范性问题：我们应该治疗谁？\n",
    " \n",
    "这是大多数企业问自己的问题，尽管措辞略有不同：我应该给谁打折？我应该对贷款收取多少利率？我应该向该用户推荐什么项目？我应该向每位客户展示什么页面布局？这些都是治疗效果异质性问题，我们可以使用第二部分中提供的工具来回答。但在我们这样做之前，我只公平地介绍一下机器学习对行业的意义，因为这将成为我们稍后将用于因果推理的基本工具。\n",
    "\n",
    "\n",
    "## 工业界中的机器学习\n",
    " \n",
    "本章的重点是谈谈我们在工业界中通常是如何使用**机器学习**的。如果您不熟悉机器学习，可以将本章视为机器学习速成课程。如果您以前从未使用过 ML，我强烈建议您至少学习基础知识，以充分利用即将发生的事情。但这并不意味着如果您已经精通机器学习，就应该跳过本章。我仍然认为你会从阅读中受益。与其他机器学习材料不同，这本书**不会**讨论决策树或神经网络等算法的来龙去脉。相反，它将专注于**机器学习在现实世界中的应用**。\n",
    " \n",
    "![img](./data/img/industry-ml/ml-meme.png)\n",
    " \n",
    "我想首先说明的是，为什么我们要在因果推理书中谈论机器学习？简短的回答是因为我认为理解因果关系的最佳方法之一是将其与机器学习带来的预测模型方法进行对比。长答案是双重的。首先，如果你已经读到本书的这一点，那么你很有可能已经熟悉机器学习。其次，即使你不是，鉴于这些主题目前的流行，你可能已经对它们是什么有了一些了解。唯一的问题是，在机器学习大肆宣传的情况下，我可能不得不带你回到现实，并用非常实用的术语解释它的真正作用。最后，因果推理的最新发展大量使用机器学习算法，所以也有。\n",
    " \n",
    "非常直接，**机器学习是一种快速、自动和良好预测的方法**。这不是全部，但它涵盖了其中的 90%。它是在有监督的机器学习领域取得了大多数很酷的进步，如计算机视觉、自动驾驶汽车、语言翻译和诊断。请注意，起初，这些看起来不像是预测任务。语言翻译是如何预测的？这就是机器学习的美妙之处。我们可以通过预测解决比最初显而易见的问题更多的问题。在语言翻译的情况下，您可以将其构建为一个预测问题，即您向机器展示一个句子，并且它必须用另一种语言预测同一个句子。请注意，我**不是**在预测或预测未来的意义上使用预测这个词。预测只是从一个定义的输入映射到一个最初未知但同样定义良好且可观察的输出。\n",
    " \n",
    "![img](./data/img/industry-ml/translation.png)\n",
    " \n",
    "机器学习真正做的是学习这个映射函数，即使它是一个非常复杂的映射函数。底线是，如果您可以将问题描述为从输入到输出的映射，那么机器学习可能是解决该问题的不错选择。至于自动驾驶汽车，你可以认为它不是一个，而是多个复杂的预测问题：从汽车前部的传感器预测车轮的正确角度，从汽车周围的摄像头预测刹车的压力，根据 gps 数据预测加速器中的压力。解决这些（以及更多）预测问题是制造自动驾驶汽车的原因。\n",
    "\n",
    "考虑 ML 的一种更具技术性的方式是估计（可能非常复杂）期望函数：\n",
    " \n",
    "$\n",
    "E[Y|X]\n",
    "$\n",
    " \n",
    "其中 \\\\(Y\\\\) 是您想知道的（翻译句子，诊断），而 \\\\(X\\\\) 是您已经知道的（输入句子，X 射线图像）。机器学习只是一种估计条件期望函数的方法。\n",
    "\n",
    "\n",
    "好的……您现在了解预测如何比我们最初想象的更强大。自动驾驶汽车和语言翻译很酷，但它们相距甚远，除非你在谷歌或优步这样的大型科技公司工作。所以，为了让事情更相关，让我们来谈谈几乎每个公司都有的问题：客户获取（即获得新客户）。\n",
    " \n",
    "从客户获取的角度来看，您通常需要做的是弄清楚谁是有利可图的客户。在这个问题中，每个客户都有获取成本（可能是营销成本、入职成本、运输成本……），并有望为公司带来正现金流。例如，假设您是一家互联网提供商或一家天然气公司。您的典型客户可能有一个看起来像这样的现金流。\n",
    " \n",
    "![img](./data/img/industry-ml/cashflow-1.png)\n",
    " \n",
    "每个条形代表您与客户关系中的一个金钱事件。例如，要立即获得客户，您需要投资于营销。然后，在某人决定与您开展业务后，您可能会产生某种入职成本（您必须向客户解释如何使用您的产品）或安装成本。只有这样，客户才开始产生每月收入。在某些时候，客户可能需要一些帮助，而您将承担维护费用。最后，如果客户决定终止合同，您可能也会为此承担一些最终成本。\n",
    " \n",
    "要查看这是否是一个有利可图的客户，我们可以在所谓的级联图中重新排列条形图。希望现金事件的总和最终高于零线。\n",
    " \n",
    "![img](./data/img/industry-ml/cascade-1.png)\n",
    " \n",
    "相反，客户很可能会产生比收入更多的成本。如果他或她很少使用你的产品并且有很高的维护需求，当我们堆积现金事件时，它们最终可能会低于零线。\n",
    " \n",
    "![img](./data/img/industry-ml/cascade-2.png)\n",
    " \n",
    "当然，这种现金流可能更简单或更复杂，具体取决于业务类型。你可以用利率做时间折扣之类的事情，然后为此疯狂，但我认为这里的重点是正确的。\n",
    " \n",
    "但是你能做些什么呢？好吧，如果你有很多盈利和非盈利客户的例子，你可以训练一个机器学习模型来识别它们。这样，您就可以将营销策略集中在只针对有利可图的客户身上。或者，如果您的合同允许，您可以在客户产生更多成本之前终止与客户的关系。本质上，您在这里所做的是**将业务问题构建为预测问题，以便您可以通过机器学习解决它**：您想要预测或识别有利可图和无利可图的客户，以便您只与有利可图的客户互动。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import ensemble\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import r2_score\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例如，假设您有 10000 个客户的 30 天交易数据。 您还需要购买 `cacq` 的成本。 如果您进行在线营销，这可能是您为他们提出的出价，也可能是运输成本或您必须与客户进行的任何培训，以便他们可以使用您的产品。 此外，为了简单起见（这是一门速成课程，而不是关于客户评估的一个学期），让我们假设您可以完全控制与您开展业务的客户。 换句话说，即使客户想与您做生意，您也有权拒绝客户。 如果是这种情况，您现在的任务就是事先确定谁将获利，因此您可以选择只与他们互动。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 32)\n"
     ]
    },
    {
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       "<p>5 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "   customer_id  cacq  day_0  day_1  day_2  day_3  day_4  day_5  day_6  day_7  \\\n",
       "0            0  -110      6      0     73     10      0      0      0     21   \n",
       "1            1   -58      0      0      0     15      0      3      2      0   \n",
       "2            2    -7      0      0      0      0      0      0      0      1   \n",
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       "4            4   -42      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   ...  day_20  day_21  day_22  day_23  day_24  day_25  day_26  day_27  \\\n",
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       "\n",
       "   day_28  day_29  \n",
       "0       0       0  \n",
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       "3       0       0  \n",
       "4       0       0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = pd.read_csv(\"data/customer_transactions.csv\")\n",
    "print(transactions.shape)\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们现在需要做的是根据这个交易数据来区分好客户和坏客户。为了简单起见，我只总结所有交易和CACQ。请记住，这会带来很多细微差别，例如区分被流失的客户和那些在一次购买和下一次购买之间处于休息状态的客户。\n",
    " \n",
    "然后，我将把这个总和（我称之为`net_value`）与客户特定的功能结合起来。由于我的目标是在决定与他们互动**之前**确定哪个客户将有利可图，因此您只能在获取期之前使用数据。在我们的例子中，这些功能是年龄，地区和收入，所有这些都可以在另一个`csv`文件中找到。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2034</td>\n",
       "      <td>33</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1859</td>\n",
       "      <td>35</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1243</td>\n",
       "      <td>26</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  region  income  age  net_value\n",
       "0            0      30    1025   24        130\n",
       "1            1      41    1649   26         10\n",
       "2            2      18    2034   33         -6\n",
       "3            3      20    1859   35        136\n",
       "4            4       1    1243   26         -8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable = (transactions[[\"customer_id\"]]\n",
    "              .assign(net_value = transactions\n",
    "                      .drop(columns=\"customer_id\")\n",
    "                      .sum(axis=1)))\n",
    "\n",
    "customer_features = (pd.read_csv(\"data/customer_features.csv\")\n",
    "                     .merge(profitable, on=\"customer_id\"))\n",
    "\n",
    "customer_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好！我们的任务正变得越来越不抽象。我们希望从非盈利客户中识别出有利可图的客户（`net_value >0`）。让我们尝试不同的东西，看看哪一个更好。但在此之前，我们需要快速了解一下机器学习（如果你知道ML是如何工作的，请随意跳过）\n",
    " \n",
    "## 机器学习速成课程\n",
    " \n",
    "出于我们的意图和目的，我们可以将ML视为一种进行预测的强力方法。为了使其正常工作，你需要一些带有标签的数据或你所预测的基本事实。然后，您可以根据该数据训练 ML 模型，并使用它来在基本事实尚未知的情况下进行预测。下图举例说明了典型的机器学习流程。\n",
    "\n",
    "![img](./data/img/industry-ml/ml-flow.png)\n",
    " \n",
    "首先，您需要知道基本事实（此处为`net_value`）的数据。然后，您训练一个 ML 模型，该模型将使用特征（在本例中为区域、收入和年龄）来预测`net_value`。此训练或估计步骤将生成一个机器学习模型，当您还没有真正的`net_value`时，该模型可用于预测`net_value`。这显示在图像的左侧。你有一些新的数据，你有特征（地区，收入和年龄），但你还不知道`net_value`。因此，您将这些数据传递到模型中，它为您提供了`net_value`预测。\n",
    " \n",
    "如果你更喜欢技术符号，那么理解机器学习的另一种方法是估计条件期望\\\\(E[Y|X]\\\\)，其中 \\\\(Y\\\\) 称为目标变量或结果，\\\\(X\\\\) 称为特征变量。ML 只是获取\\\\(\\hat{E}[Y|X]\\\\)的一种强大方式，通常通过优化一些错误或损失函数来实现。\n",
    " \n",
    "ML 模型的一个棘手问题是，它们几乎可以近似任何函数。换一种说法就是，它们可以被设计得强大到完美地拟合训练集中的数据。机器学习模型通常具有我们所说的复杂性超参数。这些东西可以调整模型的强大或复杂程度。在下图中，您可以看到简单模型（左）、中间模型（中）和复杂而强大的模型（右）的示例。请注意复杂模型如何与训练数据完美拟合。\n",
    " \n",
    "![img](./data/img/industry-ml/model-fit.png)\n",
    " \n",
    "这就提出了一些问题。也就是说，在使用我们的模型在现实世界中进行预测之前，我们如何知道模型是否有效？我们的一种方法是将预测与数据集上的实际值进行比较，其中我们有基本事实。这些是所谓的拟合优度指标，如 （R^2）。但请记住，模型可以变得如此强大，以完美地拟合数据。如果发生这种情况，预测将完全符合基本事实。这是有问题的，因为这意味着这种验证具有误导性，因为我们可以通过使我的模型更强大和复杂来保证当前数据上的验证是完美的。\n",
    " \n",
    "此外，拥有一个非常复杂的模型通常**不**是一件好事。你对为什么不好其实已经有一些直觉。例如，在上图中，您更倾向哪个模型？是更复杂，能得到所有的正确预测哪个吗？可能不是。您可能更倾向于中间的那个。它更平滑，更简单，同时，它仍然能做出一些很好的预测，即使它不是无误差地拟合了数据。\n",
    " \n",
    "![img](./data/img/industry-ml/overfitting.jpg)\n",
    " \n",
    "你的直觉是对的。如果给模型过于强大的拟合能力，会发生的情况是，它不仅会学习数据中的模式，还会学习随机噪声。但是当您使用模型在现实世界中进行预测时，噪声会有所不同（毕竟它是随机的），因此您的\"完美\"模型会犯错误。在 ML 术语中，我们说过于复杂的模型有\"过拟合\"问题，不能很好地泛化。那么，我们能做些什么呢？\n",
    " \n",
    "我们将假装我们无法访问部分数据。这个想法是将我们拥有基本事实的数据集一分为二。然后，我们可以为模型提供一个用于训练的部分，另一个部分可用于验证模型预测。这称为交叉验证。\n",
    " \n",
    "![img](./data/img/industry-ml/test.png)\n",
    " \n",
    "对于上面的数据集，模型在训练期间没有看到，因此复杂的模型不能很好地进行预测。另一方面，上面提到的图中间的模型似乎表现更好。为了选择正确的模型复杂性，我们可以训练不同的模型，每个模型具有不同的复杂性，并查看它们在一些数据上的表现，这些数据具有基本事实，但未用于训练模型。交叉验证非常重要，我们可能应该花更多的时间在上面。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉验证\n",
    " \n",
    "交叉验证对于选择模型复杂性至关重要，但除此之外，它还很有用。每当我们想尝试许多不同的事情并估计它们在现实世界中的表现时，我们都可以使用它。交叉验证的想法是模仿现实世界，在那里我们根据我们拥有的数据估计模型，但我们对新的，看不见的数据进行预测。我们假装没有的坚持数据可以作为我们将在野外遇到的代理。\n",
    "\n",
    "让我们看看如何将交叉验证应用于确定哪些客户有利可图或不有利可图的整个问题。以下是我们应该做什么的概述：\n",
    "\n",
    "1. 我们有现有客户的数据。根据这些数据，我们知道哪些是有利可图的，哪些不是（我们知道基本事实）。让我们将内部数据称为训练集。\n",
    "2. 我们将使用内部数据来*学习*一条规则，告诉我们哪个客户是有利可图的（因此培训）。\n",
    "3. 我们将规则应用于用于学习规则的维持数据。这应该模拟在一个数据集中学习规则并将其应用于另一个数据集的过程，当我们进入生产并对真正看不见的数据进行评分时，这个过程将是不可避免的。\n",
    " \n",
    "\n",
    "下面是交叉验证的图片。在图像的最右边有真正看不见的数据，然后有一些我们只是假装在学习时没有的数据。\n",
    " \n",
    " \n",
    "![img](./data/img/industry-ml/cross-validation.png)\n",
    " \n",
    "总而言之，我们将内部数据划分为训练集和测试集。我们可以使用训练集来提出模型或规则来预测客户是否盈利，但我们将在数据集的另一个分区（测试集）中验证这些规则。此测试集将隐藏在我们的学习过程中。\n",
    " \n",
    "作为旁注，除了这个简单的训练测试拆分（例如，k-fold交叉验证或时间交叉验证）之外，还有很多方法可以使交叉验证更好，但是为了我们在这里要做的事情，这就足够了。请记住，交叉验证的精神是模拟进入生产环境后会发生什么。通过这样做，我们希望得到更现实的估计。\n",
    " \n",
    "对于我们的情况，我不会做任何花哨的事情。我将数据集一分为二。70%将用于构建一种方法，使我们能够识别有利可图的客户，30%将用于评估该方法有多好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 5), (3000, 5))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test = train_test_split(customer_features, test_size=0.3, random_state=13)\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测和策略\n",
    " \n",
    "![img](./data/img/industry-ml/profit.png)\n",
    " \n",
    "我们一直在谈论识别有利可图的客户的方法和方法，但现在是我们更精确地使用我们的概念的时候了。让我们介绍两个新的。**预测**是估计或预测某事的数字。它是 \\\\(\\hat{E}[y_i|X_i]\\\\) 的估计。例如，我们可以尝试预测客户的盈利能力，预测值约为 16 巴西雷亚尔，这意味着我们预测该客户将产生 16 巴西雷亚尔的收入。这里的要点是，预测是一个简单的数字。\n",
    " \n",
    "第二个概念是**策略**。策略是自动决策规则。虽然预测是一个数字，但策略是一个决策。例如，我们可以制定一项政策，与收入超过 1000 的客户互动，否则不会参与。我们通常在预测的基础上构建策略：与盈利能力预测高于 10 且不以其他方式参与的所有客户互动， \\\\(\\hat{E}[y_i|X_i] > 10\\\\)。机器学习通常会处理第一个概念，即预测。但请注意，仅靠预测是无用的。我们需要附加一些决定或政策。\n",
    " \n",
    "我们可以做非常简单的政策和模型，也可以做非常复杂的政策和模型。对于策略和预测，我们需要使用交叉验证，即估计一个数据分区中的策略或预测，并在另一个分区中验证其有用性。由于我们已经将数据一分为二，因此我们很好。\n",
    " \n",
    "## 单特征策略\n",
    " \n",
    "在我们疯狂地研究这个盈利能力问题之前，让我们先尝试一些简单的东西。80%的收益与20%的努力的东西。他们经常创造奇迹，令人惊讶的是，大多数数据科学家都忘记了它们。那么，我们能做的最简单的事情是什么呢？当然，**与所有客户互动！**。与其弄清楚哪些客户是有利可图的，不如与每个人做生意，并希望有利可图的客户比补偿无利润的客户更多。\n",
    " \n",
    "为了检查这是否是一个好主意，我们可以看到客户的平均净值。如果结果是积极的，这意味着，平均而言，我们将在客户身上赚钱。当然，会有盈利和无利可图的，但平均而言，如果我们有足够的客户，我们就会赚钱。另一方面，如果这个值为负，这意味着如果我们与所有客户互动，我们将赔钱。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.169428571428572"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"net_value\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这太可惜了...如果我们与所有人合作，我们将为与我们开展业务的客户损失约30雷亚尔。我们的第一件非常简单的事情没有奏效，如果我们不想倒闭，我们最好找到更有前途的事情。这里只是一个简短的旁注，请记住，这是一个教学示例。虽然非常简单的\"一视同仁\"政策在这里行不通，但在现实生活中却经常如此。通常情况下，向每个人发送营销电子邮件比不发送要好，或者向每个人提供折扣券通常比不给他们更好。\n",
    " \n",
    "展望未来，我们能想到的下一件最简单的事情是什么？一个想法是采用我们的功能，看看它们是否单独区分好客户和坏客户。以`收入`为例。直觉上，更富有的客户应该更有利可图，对吧？如果我们只与最富有的客户做生意呢？这是个好主意吗？\n",
    " \n",
    "为了弄清楚这一点，我们可以将数据划分为收入分位数（分位数具有将数据划分为大小相等的分区的适当性，这就是我喜欢它们的原因）。然后，对于每个收入分位数，让我们计算平均净值。这里的希望是，尽管平均净值为负， \\\\(E[NetValue]<0\\\\)，但可能存在一些由收入定义的子人口，其中净值为正，\\\\(E[NetValue|Income=x]<0\\\\)，可能是更高的收入水平。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "np.random.seed(123) ## seed because the CIs from seaborn uses boostrap\n",
    "\n",
    "# pd.qcut create quantiles of a column\n",
    "sns.barplot(data=train.assign(income_quantile=pd.qcut(train[\"income\"], q=20)), \n",
    "            x=\"income_quantile\", y=\"net_value\")\n",
    "plt.title(\"Profitability by Income\")\n",
    "plt.xticks(rotation=70);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "而且，可悲的是，没有。然而，所有收入水平的平均净值均为负数。虽然富裕客户确实比非富裕客户\"不那么糟糕\"，但平均而言，他们仍然会产生负净值。所以收入在这里对我们没有多大帮助，但是其他变量呢，比如地区呢？如果我们的大部分成本来自不得不在遥远的地方为客户服务，我们应该期望该地区将有利可图的客户与无利可图的客户区分开来。\n",
    " \n",
    "由于 region 已经是一个分类变量，因此我们不需要在此处使用分位数。让我们看看每个地区的平均净值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "np.random.seed(123)\n",
    "region_plot = sns.barplot(data=train, x=\"region\", y=\"net_value\")\n",
    "plt.title(\"Profitability by Region\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "太棒了！我们可以清楚地看到，有些地区是盈利的，比如区域2、17、39，有些是无利可图的，比如区域0、9、29和特别差的区域26。这看起来超级有希望！我们可以将此转化为一项政策：根据我们在这里获得的数据，仅与显示有利可图的地区开展业务*\n",
    " \n",
    "需要注意的一件事是，我们正在做的是ML会做的事情，但以一种更简单的方式。也就是说，我们估计每个地区净值的预期值：\\\\(E[NetValue|Region]\\\\)。现在，我们需要接受这个估计，并用它做点什么。\n",
    " \n",
    "为了构建此策略，我们将执行一些非常简单的操作。我们将围绕一个地区的预期净值构建一个95%的置信区间。如果它大于零，我们将与该地区做生意.\n",
    " \n",
    "下面的代码生成一个字典，其中键是区域，值是 95% CI 的下限。然后，字典生成器仅筛选预期净值为正的区域。结果是我们将根据我们的政策与之开展业务的地区。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 2.9729729729729737,\n",
       " 2: 20.543302704837856,\n",
       " 4: 10.051075065003388,\n",
       " 9: 32.08862469914759,\n",
       " 11: 37.434210420891255,\n",
       " 12: 37.44213667009523,\n",
       " 15: 32.09847683044394,\n",
       " 17: 39.52753893574483,\n",
       " 18: 41.86162250217046,\n",
       " 19: 15.62406327716401,\n",
       " 20: 22.06654814414531,\n",
       " 21: 24.621030401718578,\n",
       " 25: 33.97022928360584,\n",
       " 35: 11.68776141117673,\n",
       " 37: 27.83183541449011,\n",
       " 38: 49.740709395699994,\n",
       " 45: 2.286387928016998,\n",
       " 49: 17.01853709535029}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the lower bound of the 95% CI from the plot above\n",
    "regions_to_net = train.groupby('region')['net_value'].agg(['mean', 'count', 'std'])\n",
    "\n",
    "regions_to_net = regions_to_net.assign(\n",
    "    lower_bound=regions_to_net['mean'] - 1.96*regions_to_net['std']/(regions_to_net['count']**0.5)\n",
    ")\n",
    "\n",
    "regions_to_net_lower_bound = regions_to_net['lower_bound'].to_dict()\n",
    "regions_to_net = regions_to_net['mean'].to_dict()\n",
    "\n",
    "# filters regions where the net value lower bound is > 0.\n",
    "regions_to_invest = {region: net \n",
    "                     for region, net in regions_to_net_lower_bound.items()\n",
    "                     if net > 0}\n",
    "\n",
    "regions_to_invest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`regions_to_invest`拥有我们将要接触的所有地区。现在让我们看看这个策略在我们的测试集中是如何执行的，我们假装没有这个测试集。这是评估我们政策的关键一步，因为很可能只是偶然地，我们培训集中的某个地区似乎正在盈利。如果这只是由于随机性，我们不太可能在测试集中找到相同的模式。\n",
    " \n",
    "为此，我们将筛选测试集，以仅包含定义为盈利的区域中的客户（根据我们的训练集）。然后，我们将绘制这些客户的净收入分布图，并显示我们保单的平均净收入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_policy = (test[test[\"region\"]\n",
    "                      # filter regions in regions_to_invest\n",
    "                      .isin(regions_to_invest.keys())]) \n",
    "\n",
    "sns.histplot(data=region_policy, x=\"net_value\")\n",
    "# average has to be over all customers, not just the one we've filtered with the policy\n",
    "plt.title(\"Average Net Income: %.2f\" % (region_policy[\"net_value\"].sum() / test.shape[0]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将机器学习模型作为策略的输入\n",
    " \n",
    "如果你愿意做得更好，我们现在可以使用机器学习的力量。请记住，这可能会给整个事情增加复杂性，通常只有边际收益。但是，根据具体情况，边际收益可以转化为巨额资金，这就是为什么机器学习现在如此有价值。\n",
    " \n",
    "在这里，我将使用渐变提升模型。这是一个相当复杂的模型，但使用起来非常简单。出于我们的目的，我们不需要深入了解它是如何工作的。相反，请记住我们在ML速成课程中看到的内容：ML模型是一个超级强大的预测机器，具有一些复杂性参数。它是一个估计\\\\(E[Y|X]\\\\)。越复杂，模型就越强大。但是，如果复杂性太高，模型将过度拟合，学习噪声，并且不能很好地泛化到看不见的数据。因此，我们需要在此处使用交叉验证来查看模型是否具有正确的复杂性。\n",
    " \n",
    "现在，我们需要问，如何利用良好的预测来改进我们简单的区域政策，以识别和吸引有利可图的客户？我认为，我们可以在这方面作出两项主要改进。首先，您必须同意，遍历所有功能以查找区分好客户和坏客户的功能是一个繁琐的过程。在这里，我们只有3个（年龄，收入和地区），所以它不是那么糟糕，但想象一下，如果我们有超过100个。此外，您必须小心[多次测试](https://en.wikipedia.org/wiki/Multiple_comparisons_problem)和假阳性率的问题。第二个原因是，您可能需要多个功能来区分客户。在我们的示例中，我们认为区域以外的功能也具有一些有关客户盈利能力的信息。当然，当我们单独看收入时，它并没有给我们带来多少，但是那些几乎没有盈利的地区的收入呢？也许，在这些地区，如果我们只关注更富有的客户，我们仍然可以获得一些利润。从技术上讲，我们说的是\\\\(E[NetValue|Region, Income, Age]\\\\) 是比\\\\(E[NetValue|Region]\\\\) 更好的`净值`预测因子。这很有道理。在地区之上使用更多关于收入和年龄的信息应该使我们能够更好地预测净值。\n",
    " \n",
    "提出这些涉及交互多个功能的更复杂的策略可能非常复杂。我们必须考虑的组合随着功能数量的增加而呈指数级增长，这根本不是一件实际的事情。Istead，我们能做的就是把所有这些特征都放到机器学习模型中，让它为我们学习这些交互。这正是我们下一步要做的。\n",
    " \n",
    "该模型的目标是使用`region`，`income`，`age`来预测`net_value`。为了帮助它，我们将采用区域特征（这是分类的），并使用数值对其进行编码。我们将用训练集上区域的平均净值替换每个区域。还记得我们把它们存储在`regions_to_net`字典中吗？有了这个，我们所要做的就是调用方法`.replace()`并将此字典作为参数传递。我将为此创建一个函数，因为我们将多次执行此替换。这种转换特征以促进学习的过程通常称为特征工程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode(df): \n",
    "    return df.replace({\"region\": regions_to_net})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们的模型将从 [Sklearn](https://scikit-learn.org/stable/) 导入。他们所有的模型都有一个非常标准的用法。首先，实例化传入复杂性参数的模型。对于此模型，我们将估计器的数量设置为 400，最大深度设置为 4，依此类推。模型越深，估计器的数量越多，模型就越强大。当然，我们不能让它太强大，否则它会学习训练数据中的噪声或过度拟合。同样，您不需要知道这些参数的作用的详细信息。请记住，这是一个非常好的预测模型。然后，为了训练我们的模型，我们将调用`.fit()`方法，传递特征'X'和我们要预测的变量 - 或目标变量 - `net_value`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\ensemble\\_gb.py:290: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "model_params = {'n_estimators': 400,\n",
    "                'max_depth': 4,\n",
    "                'min_samples_split': 10,\n",
    "                'learning_rate': 0.01,\n",
    "                'loss': 'ls'}\n",
    "\n",
    "features = [\"region\", \"income\", \"age\"]\n",
    "target = \"net_value\"\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "reg = ensemble.GradientBoostingRegressor(**model_params)\n",
    "\n",
    "# fit model on the training set\n",
    "encoded_train = train[features].pipe(encode)\n",
    "reg.fit(encoded_train, train[target]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在已训练该模型。接下来，我们需要检查它是否是好的。为此，我们可以在**测试集**上查看此模型的预测性能。有大量的指标可以评估机器学习模型的预测性能。在这里，我将使用一个名为 \\\\(R^2\\\\) 的函数。我们不需要在这里详细介绍。可以说\\\\(R^2\\\\)用于评估预测连续变量的模型（如`net_income`）。此外，\\\\(R^2\\\\)可以从负无穷大（如果预测值低于平均值，则为负）变为 1.0。\\\\(R^2\\\\)告诉我们`net_income`中的方差有多少是由我们的模型解释的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2:  0.7108790300152951\n",
      "Test R2:  0.6938513063048141\n"
     ]
    }
   ],
   "source": [
    "train_pred = (encoded_train\n",
    "              .assign(predictions=reg.predict(encoded_train[features])))\n",
    "\n",
    "print(\"Train R2: \", r2_score(y_true=train[target], y_pred=train_pred[\"predictions\"]))\n",
    "print(\"Test R2: \", r2_score(y_true=test[target], y_pred=reg.predict(test[features].pipe(encode))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本例中，模型解释了训练集中约 71% 的`net_income`方差，但仅解释了测试集中约 69% 的`net_income`方差。这是预料之中的。由于模型可以访问训练集，因此通常会高估那里的性能。只是为了好玩（并了解有关过拟合的更多信息），请尝试将模型的`max_depth`设置为14，看看会发生什么。您可能会看到列车 \\\\(R^2\\\\) 飙升，但测试集 \\\\(R^2\\\\) 变低。这就是过度拟合的样子。\n",
    " \n",
    "接下来，为了制定我们的策略，我们将测试集预测存储在\"预测\"列中。此预测是\\\\(E[NetValue|Age, Income, Region ]\\\\)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>region</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>net_value</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5952</th>\n",
       "      <td>5952</td>\n",
       "      <td>19</td>\n",
       "      <td>1983</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>47.734883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>1783</td>\n",
       "      <td>31</td>\n",
       "      <td>914</td>\n",
       "      <td>31</td>\n",
       "      <td>-46</td>\n",
       "      <td>-36.026935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>4811</td>\n",
       "      <td>33</td>\n",
       "      <td>1349</td>\n",
       "      <td>25</td>\n",
       "      <td>-19</td>\n",
       "      <td>22.553420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>1840</td>\n",
       "      <td>26</td>\n",
       "      <td>55</td>\n",
       "      <td>48.306256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7146</th>\n",
       "      <td>7146</td>\n",
       "      <td>19</td>\n",
       "      <td>3032</td>\n",
       "      <td>34</td>\n",
       "      <td>-17</td>\n",
       "      <td>7.039414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id  region  income  age  net_value  prediction\n",
       "5952         5952      19    1983   23         21   47.734883\n",
       "1783         1783      31     914   31        -46  -36.026935\n",
       "4811         4811      33    1349   25        -19   22.553420\n",
       "145           145      20    1840   26         55   48.306256\n",
       "7146         7146      19    3032   34        -17    7.039414"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_policy = test.assign(prediction=reg.predict(test[features].pipe(encode)))\n",
    "\n",
    "model_policy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就像我们对`region`特征所做的那样，我们可以通过对模型的预测来显示平均净值。由于模型是连续的而不是分类的，因此我们需要首先使其离散。这样做的一种方法是使用pandas的`pd.qcut`（天！我超喜欢这个函数！），它使用模型预测将数据划分为分位数。让我们使用 50 个分位数，因为 50 是我们拥有的区域数。作为惯例，我倾向于将这些模型分位数称为**模型波段**，因为它给人的直觉是，这个组在一个波段内有模型预测，比如说，从-10到200。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "n_bands = 50\n",
    "bands = [f\"band_{b}\" for b in range(1,n_bands+1)]\n",
    "\n",
    "np.random.seed(123)\n",
    "model_plot = sns.barplot(data=model_policy\n",
    "                         .assign(model_band = pd.qcut(model_policy[\"prediction\"], q=n_bands)),\n",
    "                         x=\"model_band\", y=\"net_value\")\n",
    "plt.title(\"Profitability by Model Prediction Quantiles\")\n",
    "plt.xticks(rotation=70);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里需要注意的是，有些模型波段的净值是超负的，而也有一些波段的净值非常正。此外，有些波段我们不知道净值是负数还是正数。最后，注意它们如何从左到右呈上升趋势。由于我们预测的是净收入，因此预测值将与被预测对象的实际值成正比。\n",
    "\n",
    "现在，要将使用机器学习模型的策略与仅使用区域的策略进行比较，我们还可以显示净收益的直方图以及测试集中的总净值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "model_plot_df = (model_policy[model_policy[\"prediction\"]>0])\n",
    "sns.histplot(data=model_plot_df, x=\"net_value\", color=\"C2\", label=\"model_policy\")\n",
    "\n",
    "region_plot_df = (model_policy[model_policy[\"region\"].isin(regions_to_invest.keys())])\n",
    "sns.histplot(data=region_plot_df, x=\"net_value\", label=\"region_policy\")\n",
    "\n",
    "plt.title(\"Model Net Income: %.2f;    Region Policy Net Income %.2f.\" % \n",
    "          (model_plot_df[\"net_value\"].sum() / test.shape[0],\n",
    "           region_plot_df[\"net_value\"].sum() / test.shape[0]))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如我们所看到的，该模型生成的策略比仅使用\"区域\"功能更好，但并不多。虽然模型策略将使我们在测试集上达到约16.6雷亚尔/客户，但区域政策将使我们仅获得15.5雷亚尔/客户。它只是稍微好一点，但如果你有大量的客户，这可能已经证明使用模型而不是简单的单一功能策略是合理的。\n",
    " \n",
    " \n",
    "## 细粒度策略\n",
    " \n",
    "回顾一下，到目前为止，我们测试了所有策略中最简单的策略，即与所有客户互动。此策略可以看作是估计边际净值 \\\\(\\hat{E}[NetValue] > 0\\\\)。由于这不起作用（每个客户的平均净收入为负），我们开发了一个基于区域的单一功能策略：我们将在某些区域开展业务，但在其他区域不开展业务，或者 \\\\(\\hat{E}[NetValue|Region] > 0\\\\)。这已经给了我们非常好的结果。接下来，我们进行了全面的机器学习，使用了一个使用所有功能的预测模型： \\\\(\\hat{E}[NetValue|Region, Income, Age] > 0\\\\)。然后，我们使用该模型作为策略的输入，并选择与净收入预测高于零的所有客户开展业务。\n",
    " \n",
    "在这里，策略处理的决定非常简单：与客户互动或不与客户互动。到目前为止，我们制定的政策是处理二元情况的。它们以以下形式出现\n",
    " \n",
    "```\n",
    "如果预测值>0，那么做生意，否则就不做生意。\n",
    "```\n",
    " \n",
    "这就是我们所说的**阈值**。如果预测超过某个阈值（在我们的例子中为零，但可能是其他东西），我们做出一个决定，如果没有，我们采取另一个决定。在现实生活中可以应用的另一个例子是交易欺诈检测：如果检测到欺诈的模型的预测分数高于某个阈值\"X\"，我们将拒绝交易，否则我们批准它。\n",
    " \n",
    " \n",
    "阈值在许多实际案例场景中都有效，当决策的性质是二元的时，它特别有用。但是，我们可以想到事情往往更微妙的情况。例如，您可能愿意在营销上花费更多，以引起非常有利可图的客户的注意。或者，您可能希望将他们添加到一些主要客户列表中，在那里您可以给予他们特殊待遇，但这样做也会花费更多。请注意，如果我们包括这些可能性，您的决定将从二元（参与与不参与）到持续：您应该在客户身上投入多少。\n",
    " \n",
    "在这里，对于下一个示例，假设您的决定不仅仅是与谁做生意，而是您应该在每个客户身上投入多少营销成本。为了这个例子，假设你正在与其他公司竞争，谁在特定客户身上的营销上花更多的钱，谁就赢得了这个客户（很像一个竞标机制）。在这种情况下，在高利润客户身上投入更多资金是有道理的，在利润微薄的客户身上投资得更少，在非盈利客户身上投资更少。\n",
    " \n",
    "一种方法是将你的预测分解为波段。我们之前已经这样做了，用于模型比较，但在这里我们将这样做是为了决策。让我们创建 20 个波段。我们可以将它们视为分位数或等大小的组。根据我们的预测，第一个波段将包含利润减少5%的客户*，第二个波段将包含从5%到10%的利润减少的客户，依此类推。最后一个乐队，20个，将包含最有利可图的客户。\n",
    " \n",
    "请注意，装箱也必须在训练集上进行估计，并应用于测试集！因此，我们将在训练集上使用 `pd.qcut` 计算区段。为了实际进行装箱，我们将使用`np.digitize`，传递在训练集上预先计算的箱子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>region</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>net_value</th>\n",
       "      <th>prediction</th>\n",
       "      <th>bands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5952</th>\n",
       "      <td>5952</td>\n",
       "      <td>19</td>\n",
       "      <td>1983</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>47.734883</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>1783</td>\n",
       "      <td>31</td>\n",
       "      <td>914</td>\n",
       "      <td>31</td>\n",
       "      <td>-46</td>\n",
       "      <td>-36.026935</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>4811</td>\n",
       "      <td>33</td>\n",
       "      <td>1349</td>\n",
       "      <td>25</td>\n",
       "      <td>-19</td>\n",
       "      <td>22.553420</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>145</td>\n",
       "      <td>20</td>\n",
       "      <td>1840</td>\n",
       "      <td>26</td>\n",
       "      <td>55</td>\n",
       "      <td>48.306256</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7146</th>\n",
       "      <td>7146</td>\n",
       "      <td>19</td>\n",
       "      <td>3032</td>\n",
       "      <td>34</td>\n",
       "      <td>-17</td>\n",
       "      <td>7.039414</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id  region  income  age  net_value  prediction  bands\n",
       "5952         5952      19    1983   23         21   47.734883     18\n",
       "1783         1783      31     914   31        -46  -36.026935      7\n",
       "4811         4811      33    1349   25        -19   22.553420     15\n",
       "145           145      20    1840   26         55   48.306256     18\n",
       "7146         7146      19    3032   34        -17    7.039414     13"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def model_binner(prediction_column, bins):\n",
    "    # find the bins according to the training set\n",
    "    bands = pd.qcut(prediction_column, q=bins, retbins=True)[1]\n",
    "    \n",
    "    def binner_function(prediction_column):\n",
    "        return np.digitize(prediction_column, bands)\n",
    "    \n",
    "    return binner_function\n",
    "    \n",
    "\n",
    "# train the binning function\n",
    "binner_fn = model_binner(train_pred[\"predictions\"], 20)\n",
    "\n",
    "# apply the binning\n",
    "model_band = model_policy.assign(bands = binner_fn(model_policy[\"prediction\"]))\n",
    "model_band.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FUtvgAK246YrzqiVJa250ccEb5593jZ+7b6BHNuOyfua9ddbIfnfd1tOf6rjeBNf+//SYsdfUbb2fsWx4Cw8PV25urvN5bm6uQkNDqy2XkJCghIQE5/OcnBz5m9hXTk5OtTEz78dSUz0zNeR6DXnfqFf/6pVt2CijqFi25sFqetPvTa9XWVnpfDT731Vd1gIasnbt2tU4btn7vF1yySXKzMxUdna2KioqlJKSopiYGG+3BQAuMYqKZRQUyigq9nYrACzGsjNvvr6+uvfee/Xcc8/J4XDo2muvVWRkpLfbAgAAMJVlw5sk9ejRQz169PB2GwCAn7Hb7VUeAXiWpcMbgIarfH26VFgqtQhQk0HdvN0O3GCVW60AVkV4A1A/FZbKKChR9WvI3bO81yCXlpvyznZlFRSrTWCw5rq4jpUwGwY0HIQ3APCQv8Vd6/KyU5I+UFZBodoEBmmuG+udL2bDgIbDslebAgAANEaENwAAAAshvAEAAFgI57wBcMnppGIZRQ7ZmvvIf3Cwt9sBgEaL8AbAJUaRQzrhUPVvFQYA1CXCG2BRu9+vVFmRoabNbbpsoK+323HZiivvdWm5Kf/6WlkFJWoTGKK5Lq5jJdy6A8D5IrwBFlVWZKikQBJzYR5R12GKW3cAOF+ENwAQYQqAdXC1KQAAgIUQ3gAAACyEw6ZAI7cydrFLy015a4qylKWIgNaaGzvX5K4AALVh5g0AAMBCmHkDUC9xKw0AqBnhDfCQrR9W6mSxoWbBNvW9/vzvuza2zyqXljuwfopKCrIUEhihsX0a3mFMrv4EgJoR3gAPOVlsqLhQ4r5rAAAzcc4bAACAhRDeAAAALITDpgBcwgUEAFA/EN4AuIQLCACgfiC8ocHa8HGFiooNNQ+26aZ+/K8OAGgY+ERDg1VUbKigSOLqTwBAQ0J4A87hpn6u3Xdt28YpKi7MUnBQhG7qZ/591zgHDQAaJ8IbYFGcgwYAjRO3CgEAALAQwhsAAICFEN4AAAAshPAGAABgIVywAMuJu+EVl5ZLSp6igqIsBTaLUNwN5l/9CQBAXSC8oc7845NynThlqGWQTXde28Tb7Xgct+4AANQFwhvqzIlThvKKGu4Nc7l1BwCgLnDOGwAAgIUQ3gAAACyE8AYAAGAhnPOGBosLCAAADRHhDQ0WFxAAABoiwhsuWPTA5S4t12TLFKkoS02atVH0QO67BgDA+eCcNwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhXCrENQZbpoLAMCFI7yhznDTXAAALhyHTQEAACyE8AYAAGAhhDcAAAALIbwBAABYCOENAADAQghvAAAAFkJ4AwAAsJDzCm8Oh0P5+fme7gUAAADn4NZNek+ePKkVK1bo888/l5+fn9asWaOdO3fq4MGDGj58uFk9AgAA4L/cmnlbvny5goKCtHTpUvn5ncl90dHRSklJOe8GPvvsM02ePFnDhg3ToUOHqryWlJSkhx56SA8//LDS09Od4999950eeeQRPfTQQ3rllVdkGMZ512/MFm8v0dMfntTi7SXebgUAALjIrfC2e/dujRo1SqGhoc6xFi1aqKCg4LwbiIyM1J///Gd17ty5yvixY8eUkpKi+fPna/r06Vq5cqUcDoekMyHy/vvv16JFi5SVlVUl2MF1eaccyi42lHfK4e1WAACAi9wKb0FBQSoqKqoylpOTUyXMuatDhw5q165dtfHU1FTFxcXJ399frVu3VkREhA4ePKj8/HyVlJQoOjpaNptN8fHxSk1NPe/6AAAAVuLWOW/9+vXTvHnzNHz4cBmGoW+//Vavv/66+vfv7/HG8vLyFBUV5XweFhamvLw8+fr6Kjw83DkeHh6uvLy8WreTnJys5ORkSdLs2bNlt9t1/vOE52a326uN1d6dOfUK67geAACoO26Ft0GDBsnf318rV65UZWWlXnrpJSUkJGjAgAG/uN6sWbN04sSJauPDhw9XbGxsjevUdh6bu+e3JSQkKCEhwfk8JydH/m5twT05OTnVxsy8H0tN9cxU1/UAAGisajoyKbkZ3mw2mwYOHKiBAwe6VfzJJ590a3npzIxabm6u83leXp7CwsKqjefm5iosLMzt7QMAAFiRW+Ftz549tb526aWXXnAzPxcTE6NFixbpxhtvVH5+vjIzM9WpUyf5+PgoMDBQ3377raKiorRt2zb9/ve/92htq2sx+CWXlvNJmSIVZ8knuI1aDJ5rclcAAMAT3ApvL71UNRQUFhaqoqJC4eHhWrJkyXk1sGPHDr3yyisqLCzU7NmzdfHFF2v69OmKjIxU7969NXnyZPn4+Gj06NHy8TlzAHLMmDFaunSpysvL1a1bN3Xv3v28agMAAFiNW+EtMTGxynOHw6F33nlHgYGB593AFVdcoSuuuKLG14YMGaIhQ4ZUG7/kkks0b968864JAABgVRd0Lr2Pj4+GDBmi9evXe6ofAAAA/IILvhDyq6++ch7OBAAAgLncOmw6fvz4Ks/Ly8tVXl6uMWPGeLQp1I2z92zj3m0AAFiHW+HtoYceqvK8adOmatu2rYKCgjzaFOrG1KlTvd0CAABwk1vhrUuXLmb1AQAAABecM7wtXrxYNpvtnBt68MEHPdIQAAAAanfO8BYREVEXfQAAAMAF5wxvf/zjH+uiDwAAALjArXPeJKmiokIZGRkqLCysMu7pr8cCAABAdW6Ft/3792v+/Pk6ffq0SkpKFBgYqNLS0gv6eiwAAAC4zq27665evVo333yzVq1apcDAQK1atUq33nqrrr/+erP6AwAAwM+4Fd4yMjI0YMCAKmO33HKL3n//fY82BQAAgJq5Fd6CgoJUUlIiSWrZsqWOHTum4uJilZaWmtIcAAAAqnLrnLcrr7xSX3zxhfr06aPrrrtOTz/9tHx9fdW7d2+z+gMAAMDPuBXe7rnnHufPN910k6KiolRSUqKuXbt6ui8AAADUwK3wtmPHDvXo0UN+fmdW++1vf2tKUwAAAKiZW+e8vf3227rvvvu0bNky7dmzx6yeAAAAUAu3Zt7mzp2rY8eOafv27Xr55Zd1+vRpxcXFqU+fPurYsaNZPQIAAOC/3Jp5k6QOHTpo+PDhWrx4sSZPnqyjR4/q8ccfN6M3AAAA/A+3vx5LknJycpSSkqLt27frp59+0rXXXuvpvgAAAFADt8Lbpk2btH37dh05ckTdu3fXbbfdVuUCBgAAAJjLrdSVlpam/v3764orrlBAQIBZPQEAAKAWbp3zNm3aNMXHx/9icHvkkUcuuCkAAADUzO0LFs7lp59+8vQmAQAA8F8eD282m83TmwQAAMB/caVBPfOXbenKOVUqe1CAHo/v5u12AABAPUN4q2dyTpUqq7jE220AAIB6yuOHTQ3D8PQmAQAA8F9uhbd33323xvH33nvP+fPYsWMvrCMAAADUyq3w9s4775xzvE+fPhfWEQAAAGrl0jlve/bskSQ5HA7nz2f9+OOPCgwM9HxnAAAAqMal8PbSSy9JksrLy50/S2duCxISEqJ7773XnO4AAABQhUvhLTExUZK0ZMkSPfjgg6Y2BAAAgNq5dauQBx98UBUVFTpw4IDy8/MVFxen0tJSSeK7Tn+BY5AbM5Pbv5aKS6TgEPfWAwAAjYJb4e3o0aOaM2eO/P39lZubq7i4OO3bt09bt27VpEmTzOoRAAAA/+XW1abLly/XsGHDtHDhQvn5ncl9Xbp00f79+01pDgAAAFW5Fd6OHTumq6++uspYQECAysvLPdoUAAAAauZWeGvVqpW+++67KmMHDx5URESER5sCAABAzdw6523YsGGaPXu2+vfvr4qKCiUlJenDDz/UuHHjzOoPAAAAP+PWzFvPnj01bdo0FRYWqkuXLsrJydGUKVPUtWtXs/oDAADAz7g181ZRUaFDhw7JMAwFBwerrKxMH3zwgSRx/zcPsdvtVR4BAAB+zq3wtmTJEh05ckQ9e/ZUy5YtTWqpcZs6daq3WwAAAPWYW+Htyy+/1JIlS9SsWTOz+gEAAMAvcOucN7vdrtOnT5vVCwAAAM7BrZm3+Ph4zZ07V3/4wx+qHTa99NJLPdkXAAAAauBWeNu4caMk6fXXX68ybrPZtGTJEs91BQAAgBq5Fd4SExPN6gMAAAAucOucNwAAAHgX4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAtx6ya9ZlizZo3S0tLk5+enNm3aaMKECc4vvk9KStLmzZvl4+OjUaNGqVu3bpKk7777TomJiSovL1f37t01atQo2Ww2L+4FAABA3fD6zNvll1+uefPm6YUXXlDbtm2VlJQkSTp27JhSUlI0f/58TZ8+XStXrpTD4ZAkLV++XPfff78WLVqkrKwspaene3EPAAAA6o7Xw1vXrl3l6+srSYqOjlZeXp4kKTU1VXFxcfL391fr1q0VERGhgwcPKj8/XyUlJYqOjpbNZlN8fLxSU1O9uQsAAAB1xuvh7ec2b97sPDSal5en8PBw52thYWHKy8urNh4eHu4MfAAAAA1dnZzzNmvWLJ04caLa+PDhwxUbGytJWrt2rXx9fXX11VdLkgzDqHFbtY3XJjk5WcnJyZKk2bNny263q8CtLbjHbrebuHUAANDY1Ul4e/LJJ3/x9S1btigtLU0zZsxwXngQHh6u3Nxc5zJ5eXkKCwurNp6bm6uwsLBat52QkKCEhATn85ycHPmf7464ICcnx8StAwCAxqJdu3Y1jnv9sGl6errWr1+vqVOnqmnTps7xmJgYpaSk6PTp08rOzlZmZqY6deqk0NBQBQYG6ttvv5VhGNq2bZtiYmK8uAcAAAB1x+u3Clm5cqUqKio0a9YsSVJUVJTGjh2ryMhI9e7dW5MnT5aPj49Gjx4tH58zWXPMmDFaunSpysvL1a1bN3Xv3t2buwAAAFBnbIa7J5FZXEZGhvzXbzZt+6cHXWfatgEAQONRbw+bAgAAwHWENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALMTP2w288cYb2rlzp2w2m0JCQjRhwgSFhYVJkpKSkrR582b5+Pho1KhR6tatmyTpu+++U2JiosrLy9W9e3eNGjVKNpvNi3sBAABQN7w+83bzzTfrhRde0Ny5c9WjRw/961//kiQdO3ZMKSkpmj9/vqZPn66VK1fK4XBIkpYvX677779fixYtUlZWltLT0724BwAAAHXH6+EtKCjI+XNZWZlzBi01NVVxcXHy9/dX69atFRERoYMHDyo/P18lJSWKjo6WzWZTfHy8UlNTvdU+AABAnfL6YVNJev3117Vt2zYFBQVp5syZkqS8vDxFRUU5lwkLC1NeXp58fX0VHh7uHA8PD1deXl6t205OTlZycrIkafbs2bLb7SowaT8kyW63m7h1AADQ2NVJeJs1a5ZOnDhRbXz48OGKjY3V7bffrttvv11JSUnauHGjhg4dKsMwatxWbeO1SUhIUEJCgvN5Tk6O/N3agntycnJM3DoAAGgs2rVrV+N4nYS3J5980qXl+vTpo9mzZ2vo0KEKDw9Xbm6u87W8vDyFhYVVG8/NzXVe4AAAANDQef2ct8zMTOfPO3fudKbMmJgYpaSk6PTp08rOzlZmZqY6deqk0NBQBQYG6ttvv5VhGNq2bZtiYmK81T4AAECd8vo5b//4xz+UmZkpm80mu92usWPHSpIiIyPVu3dvTZ48WT4+Pho9erR8fM5kzTFjxmjp0qUqLy9Xt27d1L17d2/uAgAAQJ2xGe6eRGZxGRkZ8l+/2bTtnx50nWnbBgAAjUdt57x5/bApAAAAXEd4AwAAsBCvn/NW3z23bZN+OlWsVkHBmh5/g7fbAQAAjRzh7Rx+OlWsrOJCb7cBAAAgicOmAAAAlkJ4AwAAsBDCGwAAgIUQ3gAAACyE8AYAAGAhhDcAAAALIbwBAABYCOENAADAQghvAAAAFkJ4AwAAsBDCGwAAgIUQ3gAAACyE8AYAAGAhhDcAAAAL8fN2A95wetB1ri+8/d9ScaEUHOTeegAAACZg5g0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFiIn7cbqO/sdnuVRwAAAG+yGYZheLuJupSRkeHtFgAAAM6pXbt2NY5z2BQAAMBCCG8AAAAWQngDAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwEMIbAACAhTS6L6YHAACwMmbeXPDYY4812HoNed+oRz3qea9eQ9436lHP2/UIbwAAABZCeAMAALAQwpsLEhISGmy9hrxv1KMe9bxXryHvG/Wo5+16XLAAAABgIcy8AQAAWIiftxuoz5YuXapdu3YpJCRE8+bNM7VWTk6OEhMTdeLECdlsNiUkJGjAgAGm1SsvL9fMmTNVUVGhyspK9erVS0OHDjWt3lkOh0OPPfaYwsLCTL/a54EHHlBAQIB8fHzk6+ur2bNnm1rv5MmTWrZsmX744QfZbDaNHz9e0dHRptTKyMjQggULnM+zs7M1dOhQDRw40JR67733njZv3iybzabIyEhNmDBBTZo0MaWWJH3wwQf6+OOPZRiG+vXr5/H9qul3u7i4WAsWLNBPP/2kVq1aadKkSQoODjat3meffaa3335bx48f1/PPP69LLrnEI7Vqq7dmzRqlpaXJz89Pbdq00YQJE9SsWTPT6r3xxhvauXOnbDabQkJCNGHCBIWFhZlW76x3331Xf//737VixQq1aNHCtHpvvfWWPv74Y2eN22+/XT169DCtniT9+9//1saNG+Xr66sePXrorrvuMq3eggULlJGRIUk6deqUgoKCNHfuXNPqHT58WMuXL1d5ebl8fX01ZswYderUyfR6paWlatWqlSZOnKigoKALrlXbZ7nH318M1Grv3r3GoUOHjMmTJ5teKy8vzzh06JBhGIZx6tQpY+LEicYPP/xgWj2Hw2GUlJQYhmEYp0+fNh5//HHjm2++Ma3eWRs2bDAWLlxo/OUvfzG91oQJE4yCggLT65y1ePFiIzk52TCMM/9Oi4uL66RuZWWlMWbMGCM7O9uU7efm5hoTJkwwysrKDMMwjHnz5hmffPKJKbUMwzCOHDliTJ482SgtLTUqKiqMZ555xsjIyPBojZp+t9esWWMkJSUZhmEYSUlJxpo1a0yt98MPPxjHjx83Zs6caRw8eNBjtWqrl56eblRUVBiGcWZfzd6/kydPOn9+//33jZdfftnUeoZhGD/99JPx7LPPGuPHj/fo735N9d58801j/fr1Hqtxrnq7d+82nnnmGaO8vNwwDMM4ceKEqfV+bvXq1cbbb79tar1Zs2YZu3btMgzDMNLS0oyZM2eaWu+xxx4z9u7daxiGYXz88cfG66+/7pFatX2We/r9hcOmv6BLly4e+8v7XEJDQ9WxY0dJUmBgoNq3b6+8vDzT6tlsNgUEBEiSKisrVVlZKZvNZlo9ScrNzdWuXbvUr18/U+t4w6lTp/T111/ruuuukyT5+fl5bFbjXHbv3q2IiAi1atXKtBoOh0Pl5eWqrKxUeXm5QkNDTat1/PhxRUVFqWnTpvL19VXnzp21Y8cOj9ao6Xc7NTVVffv2lST17dtXqampptbr0KGD2rVr57Ea56rXtWtX+fr6SpKio6M9+v5SU72fz2KUlZV59P2ltvfm1atX68477/T4e1ldfhbUVu/DDz/UoEGD5O/vL0kKCQkxtd5ZhmHos88+01VXXWVqPZvNppKSEkln3k89+R5TU72MjAx17txZknT55ZfrP//5j0dq1fZZ7un3Fw6b1kPZ2dn6/vvvPTZlXBuHw6GpU6cqKytLN9xwg6Kiokyt9+qrr+quu+5y/oLWheeee06S1L9/f1OvLsrOzlaLFi20dOlSHTlyRB07dtQ999zjDMhm+vTTTz36xvq/wsLCdNNNN2n8+PFq0qSJunbtqq5du5pWLzIyUm+88YaKiorUpEkTffHFFx49pFibgoIC5wdGaGioCgsLTa/pLZs3b1ZcXJzpdV5//XVt27ZNQUFBmjlzpqm1du7cqbCwMF188cWm1vm5TZs2adu2berYsaPuvvtuUwNeZmam9u/frzfeeEP+/v4aMWKE6Z8RkvT1118rJCREbdu2NbXOyJEj9dxzz2nNmjVyOBx69tlnTa0XGRmpnTt3KjY2Vp9//rlyc3M9XuPnn+Wefn9h5q2eKS0t1bx583TPPfd45Pj7L/Hx8dHcuXO1bNkyHTp0SEePHjWtVlpamkJCQpx/kdSFWbNmac6cOZo2bZo2bdqkffv2mVarsrJS33//va6//nr99a9/VdOmTbVu3TrT6p1VUVGhtLQ09erVy7QaxcXFSk1NVWJiol5++WWVlpZq27ZtptXr0KGDBg0apGeffVbPP/+8fvWrX8nHh7cqT1m7dq18fX119dVXm17r9ttv10svvaQ+ffpo48aNptUpKyvT2rVrNWzYMNNq/K/rr79eixcv1l//+leFhobqtddeM7Wew+FQcXGxnnvuOY0YMUILFiyQUQc3izD7j8OzPvzwQ40cOVIvvfSSRo4cqWXLlplab/z48dq0aZOmTp2qkpIS+fl5di7L7M9y3hHrkYqKCs2bN09XX321rrzyyjqr26xZM3Xp0kXp6emm1fjmm2+0c+dOPfDAA1q4cKH27NmjRYsWmVZPkvPk6JCQEMXGxurgwYOm1QoPD1d4eLhz9rJXr176/vvvTat31hdffKFf//rXatmypWk1du/erdatW6tFixby8/PTlVdeqW+//da0epJ03XXXac6cOXr66acVHBxs+l/90pn/T/Lz8yVJ+fn5HjvZvT7ZsmWL0tLSNHHiRNNPk/i5Pn36eOywVE1+/PFHZWdna8qUKXrggQeUm5urqVOn6sSJE6bVbNmypXx8fOTj46N+/frp0KFDptWSzryfXXnllbLZbOrUqZN8fHxUVFRkas3Kykrt2LGjTmZpt27d6vzc6927t6nv15LUvn17PfHEE5ozZ46uuuoqtWnTxmPbrumz3NPvL4S3esIwDC1btkzt27fXjTfeaHq9wsJCnTx5UtKZK093796t9u3bm1bvjjvu0LJly5SYmKg//elPuvTSSzVx4kTT6pWWljoPz5aWluqrr77SRRddZFq9li1bKjw83Hl11u7du9WhQwfT6p1VF38V2+12HThwQGVlZTIMw/T/V6QzhzClM1du7dixo07+8o+JidHWrVslnfkgiY2NNb1mXUpPT9f69es1depUNW3a1PR6mZmZzp937txp2vl9knTRRRdpxYoVSkxMVGJiosLDwzVnzhxT/6g5+0EsSTt27FBkZKRptSQpNjZWe/bskXTmfK2Kigo1b97c1Jq7d+9Wu3btFB4ebmod6Uw4PXt0ZM+ePYqIiDC13tn3GIfDobVr16p///4e2W5tn+Wefn/hJr2/YOHChdq3b5+KiooUEhKioUOHOk9I97T9+/drxowZuuiii5x/EXvy0vP/deTIESUmJsrhcMgwDPXu3Vu33XabKbX+1969e7VhwwZTbxXy448/6oUXXpB05q/HPn36aMiQIabVk85cer5s2TJVVFSodevWmjBhgqnnwJSVlWn8+PFasmSJ6YfY33rrLaWkpMjX11cXX3yxxo0b5zxx2gwzZsxQUVGR/Pz8dPfdd+uyyy7z6PZr+t2OjY3VggULlJOTI7vdrsmTJ3vsv19N9YKDg/XKK6+osLBQzZo108UXX6zp06ebVi8pKUkVFRXOfYqKitLYsWNNq7dr1y5lZmbKZrPJbrdr7NixHrtVyLnemx944AH95S9/8djsaU319u7dq8OHD8tms6lVq1YaO3asx06yr6lefHy885xaPz8/jRgxQpdeeqlp9a677jolJiYqKipK119/vUfq/FK9du3aadWqVXI4HPL399eYMWM8dppNTfVKS0u1adMmSdIVV1yhO+64wyOz0bV9lkdFRXn0/YXwBgAAYCEcNgUAALAQwhsAAICFEN4AAAAshPAGAABgIYQ3AAAACyG8AWi0HnjgAX311VcNpg6AxoHwBgAAYCGENwAAAAvx7DexAoDFHDp0SKtWrdKJEycUGxurMWPGqLy8XEuWLNGBAwfkcDj0m9/8Rvfdd5/za4Keeuop/fa3v9XevXt15MgRRUdHa+LEic47+m/btk1vvPGGSktLq33d3cGDB7VixQplZmaqSZMm6tOnj0aOHFnn+w3Auph5A9Cobd++XdOnT9fixYuVmZmptWvXyjAMXXPNNVq6dKmWLl2qJk2aaOXKlVXW+/TTTzV+/HitWLFCFRUV2rBhgyTp2LFjWr58uR588EG9/PLLKioqUm5urnO9VatWacCAAVq9erUWL16s3r171+n+ArA+whuARu2GG26Q3W5XcHCwBg8erE8//VTNmzdXr1691LRpUwUGBmrIkCH6+uuvq6x3zTXXqF27dmrSpIl69+6tw4cPS5I+//xz9ezZU126dJG/v7+GDRtW5TsT/fz8lJWVpcLCQgUEBCg6OroudxdAA8BhUwCNmt1ud/7cqlUr5eXlqaysTKtXr1Z6erpOnjwpSSopKZHD4ZCPz5m/eVu2bOlcr2nTpiotLZUk5eXlOQ+vSlJAQICaN2/ufD5u3Di9+eabmjRpklq3bq3bbrtNPXv2NHMXATQwhDcAjVpOTk6Vn8PCwrRhwwZlZGTo+eefV8uWLXX48GE9+uijMgzjnNsLDQ3V8ePHnc/LyspUVFTkfN62bVv96U9/ksPh0I4dOzR//nytXLlSAQEBnt0xAA0Wh00BNGqbNm1Sbm6uiouLlZSUpN69e6u0tFRNmjRRUFCQiouL9fbbb7u8vV69eiktLU379+9XRUWF3nzzzSqhb9u2bSosLJSPj4+CgoIkyTmbBwCuYOYNQKPWp08fPfvss8rPz1dMTIxuvfVWnTx5UosWLdLo0aMVFhamG2+8UampqS5tLzIyUqNHj9aLL76osrIy3XjjjVUOo6anp+u1115TWVmZWrVqpYcfflhNmjQxa/cANEA2w5XjAAAAAKgXmKsHAACwEMIbAACAhRDeAAAALITwBgAAYCGENwAAAAshvAEAAFgI4Q0AAMBCCG8AAAAWQngDAACwkP8P/dqaYkSSQYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(data=model_band, x=\"bands\", y=\"net_value\")\n",
    "plt.title(\"Model Bands\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这些频段，我们可以将大部分营销投资分配给20和19频段。请注意，我们是如何从二元决策（参与与不参与）转变为连续决策：为每个客户在营销上投入多少资金。当然，您可以进一步微调它，添加更多波段。在限制中，您根本没有装箱。相反，您正在使用模型的原始预测，并且可以创建决策规则，例如\n",
    " \n",
    "```\n",
    "mkt_investments_i = model_prediction_i * 0.3\n",
    "```\n",
    " \n",
    "对于每个客户\\\\(i\\\\)，您投资了模型预测的net_value的30%（30%是任意数字，但您明白了）。\n",
    "\n",
    " \n",
    "## 关键思想\n",
    " \n",
    "我们在很短的时间内已经在这里涵盖了很多领域，所以我认为这个回顾对于我们看看我们在这里取得的成就非常重要。首先，我们了解到大多数机器学习应用程序只涉及做出良好的预测，其中预测被理解为从已知输入到最初未知但定义明确的输出的映射。我们也可以把预测理解为估计一个期望函数 \\\\(E[Y|X]\\\\)。但当我说\"仅此而已\"时，我并不完全公平。我们还看到了好的预测可以解决比我们乍一看可能意识到的更多的问题，比如语言翻译和自动驾驶汽车。\n",
    " \n",
    "\n",
    "然后，我们回到现实，看看好的预测如何帮助我们完成更常见的任务，比如弄清楚我们应该引入哪个客户以及避免哪些客户。具体来说，我们研究了如何预测客户利润。根据这一预测，我们制定了一项政策，决定我们应该与谁做生意。请注意，这只是可以应用预测模型的示例。肯定还有其他应用的基调，例如信用卡承销，欺诈检测，癌症诊断以及任何可能有用的良好预测。\n",
    "\n",
    "这里的关键点是，**如果你能将你的业务问题描述为预测问题，那么机器学习可能是这项工作的正确工具**。我真的想多强调这一点。随着围绕机器学习的所有炒作，我觉得人们忘记了这一非常重要的一点，并且经常最终制作出非常擅长预测完全无用的东西的模型。他们不是考虑如何将业务问题构建为预测问题，然后用机器学习来解决它，而是经常构建一个预测模型，并试图看看哪些业务问题可以从该预测中受益。这可能有效，但通常情况下，这是一个黑暗中的镜头，只会在寻找问题时生成解决方案。\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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